TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build complex AI models. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. TensorLayer can also be found at iHub and Gitee.

🔥📰🔥 Reinforcement Learning Model Zoos: Low-level APIs for Research and High-level APIs for Production

🔥📰🔥 Sipeed Maxi-EMC: Run TensorLayer models on the low-cost AI chip (e.g., K210) (Alpha Version)

🔥📰🔥 NNoM: Run TensorLayer quantized models on the MCU (e.g., STM32) (Coming Soon)

🔥📰🔥 Free GPU and Data Storage from SurgicalAI: SurgicalAI is sponsoring the TensorLayer Community with Cloud Computing Resources such as Free GPUs and Data Storage.

As deep learning practitioners, we have been looking for a library that can address various development purposes. This library is easy to adopt by providing diverse examples, tutorials and pre-trained models. Also, it allow users to easily fine-tune TensorFlow; while being suitable for production deployment. TensorLayer aims to satisfy all these purposes. It has three key features:

• Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

• Flexibility : TensorLayer APIs are transparent: it does not mask TensorFlow from users; but leaving massive hooks that help low-level tuning and deep customization.

• Zero-cost Abstraction : TensorLayer can achieve the full power of TensorFlow. The following table shows the training speeds of VGG16 using TensorLayer and native TensorFlow on a TITAN Xp.

  Mode	Lib	Data Format	Max GPU Memory Usage(MB)	Max CPU Memory Usage(MB)	Avg CPU Memory Usage(MB)	Runtime (sec)
  AutoGraph	TensorFlow 2.0	channel last	11833	2161	2136	74
  	Tensorlayer 2.0	channel last	11833	2187	2169	76
  Graph	Keras	channel last	8677	2580	2576	101
  Eager	TensorFlow 2.0	channel last	8723	2052	2024	97
  	TensorLayer 2.0	channel last	8723	2010	2007	95

TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in both English and Chinese.

If you want to try the experimental features on the the master branch, you can find the latest document here.

You can find a large collection of tutorials, examples and real-world applications using TensorLayer within examples or through the following space:

TensorLayer 2.0 relies on TensorFlow, numpy, and others. To use GPUs, CUDA and cuDNN are required.

Install TensorFlow:

pip3 install tensorflow-gpu==2.0.0-rc1 # TensorFlow GPU (version 2.0 RC1)
pip3 install tensorflow # CPU version

Install the stable release of TensorLayer:

pip3 install tensorlayer

Install the unstable development version of TensorLayer:

pip3 install git+https://github.com/tensorlayer/tensorlayer.git

If you want to install the additional dependencies, you can also run

pip3 install --upgrade tensorlayer[all]              # all additional dependencies
pip3 install --upgrade tensorlayer[extra]            # only the `extra` dependencies
pip3 install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies

The following table shows the training speeds of VGG16 using TensorLayer and native TensorFlow on a TITAN Xp.

Please read the Contributor Guideline before submitting your PRs.

If you use TensorLayer for any projects, please cite this paper：

@article{tensorlayer2017,
    author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
    journal = {ACM Multimedia},
    title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
    url     = {http://tensorlayer.org},
    year    = {2017}
}

TensorLayer is released under the Apache 2.0 license.